Update app.py

app.py

@@ -902,7 +902,7 @@ with ui.navset_card_tab(id="tab"):
         ui.panel_title("Kmer Distribution")
         with ui.layout_columns():
             with ui.card():
-                ui.input_slider("kmer", "kmer", 0, 10, 0)
+                ui.input_slider("kmer", "kmer", 0, 10, 4)
                 ui.input_slider("top_k", "top:", 0, 1000, 15)
                 
                 ui.input_selectize(  
@@ -941,37 +941,38 @@ with ui.navset_card_tab(id="tab"):
                 ax.set_xticklabels(df['kmer'], rotation=90)
             return fig
         #ui.output_plot("plot_micro_output")
-    # with ui.nav_panel("Viral Model Training"):    
-    #     ui.page_opts(fillable=True)
-    #     ui.panel_title("Does context size matter for a nucleotide model?")
+    with ui.nav_panel("Viral Model Training"):    
+        ui.page_opts(fillable=True)
+        ui.panel_title("Does context size matter for a nucleotide model?")
         
-    #     def plot_loss_rates(df, type): 
-    #         # interplot each column to be same number of points
-    #         x = np.linspace(0, 1, 1000)
-    #         loss_rates = []
-    #         labels = ['32', '64', '128', '256', '512', '1024']
-    #         #drop the column step
-    #         df = df.drop(columns=['Step'])
-    #         for col in df.columns:
-    #             y = df[col].dropna().astype('float', errors = 'ignore').dropna().values
-    #             f = interp1d(np.linspace(0, 1, len(y)), y)
-    #             loss_rates.append(f(x))
-    #         fig, ax = plt.subplots()
-    #         for i, loss_rate in enumerate(loss_rates):
-    #             ax.plot(x, loss_rate, label=labels[i])
-    #         ax.legend()
-    #         ax.set_title(f'Loss rates for a {type} parameter model')
-    #         ax.set_xlabel('Training steps')
-    #         ax.set_ylabel('Loss rate')
-    #         return fig
-            
-    #     @render.plot
-    #     def plot():
-    #         fig = None
-    #         df = pd.read_csv('14m.csv')
-    #         mpl.rcParams.update(mpl.rcParamsDefault)
-    #         fig = plot_loss_rates(df, '14M')
-    #         return fig
+        def plot_loss_rates(df, type): 
+            # interplot each column to be same number of points
+            x = np.linspace(0, 1, 1000)
+            loss_rates = []
+            labels = ['32', '64', '128', '256', '512', '1024']
+            #drop the column step
+            df = df.drop(columns=['Step'])
+            for col in df.columns:
+                y = df[col].dropna().astype('float', errors = 'ignore').dropna().values
+                f = interp1d(np.linspace(0, 1, len(y)), y)
+                loss_rates.append(f(x))
+            fig, ax = plt.subplots()
+            for i, loss_rate in enumerate(loss_rates):
+                ax.plot(x, loss_rate, label=labels[i])
+            ax.legend()
+            ax.set_title(f'Loss rates for a {type} parameter model')
+            ax.set_xlabel('Training steps')
+            ax.set_ylabel('Loss rate')
+            return fig
+        
+        import matplotlib as mpl
+        @render.plot()
+        def plot_context_size_scaling():
+            fig = None
+            df = pd.read_csv('14m.csv')
+            mpl.rcParams.update(mpl.rcParamsDefault)
+            fig = plot_loss_rates(df, '14M')
+            return fig
 
         
 # @render.image